Many industries now depend heavily on automated manufacturing systems. A subset of an automated manufacturing system is an automated material handling system (AMHS) that moves work-in-process through various processing steps that take place in one or more bays in a manufacturing or warehousing facility. A typical handling system includes mobile components such as inter-bay vehicles that carry work-in-process between work stations within the same bay, intra-bay vehicles that carry work-in-process between different bays, and work-in-process storage systems called “stockers.” The handling system can also include various stationary devices, such as robots, that perform some sort of operation on the work-in-process. For example, robots might load and unload work in process from a vehicle.
Obtaining maximum manufacturing throughput with an AMHS requires that the motion parameters (e.g., velocity, acceleration) of the various components of the system be carefully orchestrated and optimized. In existing systems, motion parameters are not monitored in real time or near real time to proactively identify potential problems. Usually, relevant motion parameters are set through an initial run or calibration, and then the system is allowed to run until something goes wrong. Only when something goes wrong do the operators know there is a problem. Things that can go wrong include lack of synchronization of moving vehicles resulting in increased traffic congestion leading to decreased throughput, vibration of vehicles, robots or other components resulting in damage to work-in process, and the like. Usually these problems are created by factors such as misalignment, normal wear-and-tear of the system, faulty component design, and human error.
Existing sensors for characterizing motion parameters are used retroactively to try to find the source of the problem once the AMHS has failed. These instruments have several fundamental shortcomings. They are expensive and also are large so that only one type of sensor can be mounted at a time. They are also so massive that they can alter the mass characteristics of the device whose motion they measure such that it's not clear what is being measured. They also have very limited capabilities. For example, they have no capability to transmit and display real-time data to a remote location. Instead, they rely on recording devices to record motion parameters for a fixed period of time; the collected motion data must then be downloaded from the recording devices and processed manually very periodically. Because they do not operate in real time, they cannot proactively predict or address equipment downtime issues. Moreover, they have no capability to network together multiple sensors, including sensors of different types.
Existing sensors for characterizing motion parameters also have several less-fundamental shortcomings. For example, they have limited data storage capacity; they do not time-stamp the actual data being monitored, making time-based analysis and cross-correlation impossible; they are customized for one type of sensor; they measure only composite values of motion parameters (vector addition of motion along the three axes), rather than values along multiple axes at the same time, and cannot measure velocities; they are manually intensive to set up and use; they cannot be integrated with factory control systems to provide downtime event synchronization; and they have no capability to identify potential safety and equipment failure events.